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Autor:
Jarne Verhaeghe, Thomas De Corte, Christopher M. Sauer, Tom Hendriks, Olivier W.M. Thijssens, Femke Ongenae, Paul Elbers, Jan De Waele, Sofie Van Hoecke
Publikováno v:
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
International Journal of Medical Informatics, 175:105086. Elsevier Ireland Ltd
Verhaeghe, J, de Corte, T, Sauer, C M, Hendriks, T, Thijssens, O W M, Ongenae, F, Elbers, P, de Waele, J & van Hoecke, S 2023, ' Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients ', International Journal of Medical Informatics, vol. 175, 105086 . https://doi.org/10.1016/j.ijmedinf.2023.105086
International Journal of Medical Informatics, 175:105086. Elsevier Ireland Ltd
Verhaeghe, J, de Corte, T, Sauer, C M, Hendriks, T, Thijssens, O W M, Ongenae, F, Elbers, P, de Waele, J & van Hoecke, S 2023, ' Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients ', International Journal of Medical Informatics, vol. 175, 105086 . https://doi.org/10.1016/j.ijmedinf.2023.105086
Background: Atrial Fibrillation (AF) is the most common arrhythmia in the intensive care unit (ICU) and is associated with increased morbidity and mortality. Identification of patients at risk for AF is not routinely performed as AF prediction models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a202e98336c92570c5074b6bd71ff6c2
https://biblio.ugent.be/publication/01H113A35SMY5KN5ZFDMD3SA3S/file/01H11SBMXA8H225D2R2PKH44NS
https://biblio.ugent.be/publication/01H113A35SMY5KN5ZFDMD3SA3S/file/01H11SBMXA8H225D2R2PKH44NS